In-Store Mobile Phone Use and Customer Shopping Behavior

Jarrod Griffin

6/23/2025

In‑Store Mobile Phone Use & Consumer Shopping Behavior: Evidence from the Field (Grewal et al., 2018)

Grewal, Dhruv, Carl-Philip Ahlbom, Lauren Beitelspacher, Stephanie M. Noble, and Jens Nordfält (2018), “In-Store Mobile Phone Use and Customer Shopping Behavior: Evidence from the Field,Journal of Marketing, 82 (4), 102–126.

Presentation Road‑Map

  1. Introduction & Motivation
  2. Theory & Hypotheses
  3. Study 1 — Method & Results
  4. Study 2 — Method & Results
  5. Integrated Discussion & Managerial Implications
  6. Limitations & Future Research
  7. Class Discussion Prompts

Introduction

Problem / Motivation

  • Mobile ubiquity: 95 % of U.S. consumers own a mobile phone and 77 % a smartphone (Pew Research Center 2018); penetration ≈ 65 % worldwide and 84 % in Europe (GSMA 2017).
  • Screen time: Adults log ~6 h/day of digital media use; nearly half is on mobile devices (eMarketer 2017).
  • In‑store paradox: Phones can create “mobile blinders” distracting shoppers from impulse displays and lowering recall (Bellini & Aiolfi 2017; Atalay, Bodur, & Bressoud 2017). Yet they also enable richer searches and mobile coupon redemption (Burke 2002; Hui et al. 2013).
  • Retailer dilemma: Discourage phone use to protect impulse sales or embrace it to enhance engagement?
  • Research gap: No comprehensive field evidence on how ordinary in‑store phone use affects overall sales and shopper satisfaction.

Research Objectives

  • RO1: Does mobile phone use in stores influence purchases?
  • RO2: Identify behavioral mechanisms (time, visual attention, path diversion) responsible for this effect.
  • RO3: What are the boundary conditions for the mobile-phone effect?
  • RO4: Does distraction due to mobile phone use decrease or increase customer satisfaction with the shopping experience?

Significance of the Study

  • Fills a critical gap: First field evidence on everyday in‑store phone use and total sales, beyond coupons/impulse aisles.
  • Advances theory: Extends limited‑attention framework—shows how distraction can increase spending via time, fixations, and route deviations.
  • Actionable for managers: Proves retailers can encourage phone use (Wi‑Fi, QR codes) to boost revenue without hurting satisfaction.
  • Methodological benchmark: 110 h of eye‑tracking matched to receipts across 411 trips—largest dataset of its kind.
  • Influential already: ≈ 275 citations (Google Scholar, 23 Jun 2025); cornerstone for frictionless & omnichannel retail research.

Hypotheses 

Time Path — Prior Evidence

  • Attention diversion slows tasks: When cognitive resources shift to phones, shoppers neglect lists/goals, prolonging trips (Block & Morwitz 1999; Inman, Winer, & Ferraro 2009).
  • Longer dwell → larger baskets: Empirical store‑tracking work links minutes in aisle to higher spend (Sorensen 2003).

Hypothesis 1

H1a. Mobile phone use increases total time spent in the store.
H1b. More time in store increases purchases.

Shelf‑Attention Path — Prior Evidence

  • Pause = extra visual sweep: Phone‑induced stops grant shoppers seconds to scan nearby products (Atalay, Bodur, & Bressoud 2017).
  • Visual‑attention work shows even micro‑fixations raise product awareness (Anstis 1998; Pieters & Wedel 2012).

Hypothesis 2

H2a. Mobile phone use increases shelf attention.
H2b. Greater shelf attention increases purchases.

Loop‑Diversion Path — Prior Evidence

  • Shoppers follow spatial scripts around the perimeter; distraction prompts deviations/back‑tracking (Bower, Black, & Turner 1979).
  • Deviations expose consumers to otherwise unseen SKUs, boosting unplanned buys (Hui et al. 2013).

Hypothesis 3

H3a. Mobile phone use increases loop diversion.
H3b. Greater loop diversion increases purchases.

Mediation Test — Integrating the Paths

  • Limited‑capacity theory (Repovš & Baddeley 2006) predicts that time, attention, and path shifts collectively transmit distraction effects to spending.

Hypothesis 4

H4. Time in store, shelf attention, and loop diversion jointly mediate the positive relationship between phone use and purchases.

Study 1 — Naturalistic Field Study

Study Design

  • Context: Real‑life grocery shopping trips in four suburban Stockholm supermarkets (~36 140 ft² each).
  • Method: Eye‑tracking field study — portable Tobii Pro glasses record visual field & gaze.
  • Purpose: Capture natural shopper behavior; maximize ecological validity vs. lab scenarios.

Sampling & Data Collection

  • Recruitment: Research staff at store entrances (Mon–Sun, 09:00–17:00).
  • Intercept every passer‑by; offer coupon incentive; no mention of phone focus.
  • Initial recruits: 393 shoppers → 359 with usable video → 294 final (completed exit survey).

Procedure

  1. Consent & fit Tobii glasses.
  2. Shopper completes trip “as usual.”
  3. Staff collects glasses & receipt at checkout.
  4. Exit survey: demographics + satisfaction.
  5. Coders transform 90 h of video into gaze & movement metrics.

Sample Characteristics

  • n = 294 | Age 18‑73 (M = 41.5).
  • Gender: 39.5 % female.
  • No demographic differences across stores or phone‑use groups.

Measures

Role Variable Operationalization
IV Mobile phone use Binary (0 = none, 1 = used; duration captured)
Mediators Total time in store Minutes from entry to checkout
Shelf attention Count of analytical fixations on products/price tags
Loop diversion Back‑tracks off natural customer loop (#)
DV Purchases Basket value (SEK) and item count
Control Satisfaction 1‑7 overall visit rating

Stimuli & Equipment

  • Stimulus set: The live store environment (thousands of SKUs).
  • Equipment: Tobii Pro Glasses; Figure 1 illustrates visual field & fixations.

Figure 1

Mediation Model

Models Tested in Study 1

Study 1 Results (Key Paths)

  • Mobile users spent +4.6 min in store (p < .001).
  • Shelf fixations +17 % (p = .02).
  • Loop diversions +0.97 (p < .001).
  • Basket value +120 SEK (~$14) (p = .007).
  • Indirect effects via all three mediators significant.

Study 2 — Field Experiment

Purpose & Design

  • Goal 1: Replicate Study 1 findings with fresh sample.
  • Goal 2: Eliminate self‑selection bias via random assignment to phone‑use vs no‑use condition.
  • Goal 3: Directly measure distraction to test limited‑attention theory (4‑item, α =.90).

Sampling & Random Assignment

  • Setting: Two suburban grocery stores (same chain).
  • Recruitment: Every 5th passer‑by at a predefined entrance point; scratch‑off lottery ticket incentive.
  • Eligibility: Must carry a mobile phone (4 shoppers excluded).
  • Sample: 121 recruited → 117 usable (24 h of eye‑tracking video).
  • Randomization: Assigned to Phone‑Allowed (n = 53) or Phone‑Prohibited (n = 64) groups.

Procedure

  1. Consent, demographic pre‑survey.
  2. Fit Tobii Pro glasses.
  3. Condition instructions:
    • Use group: “Please use your smartphone as you normally would—email, browse, play, etc.”
    • No‑use group: “Please refrain from all phone use during this trip.”
  4. Shopper completes trip.
  5. Collect glasses & receipt.
  6. Post‑trip survey: distraction scale, satisfaction, demographics.

Sample Characteristics

  • n = 117 | Age 19‑80 (M = 42.9).
  • Gender: 52.1 % female.
  • No age, gender, or household‑size differences across conditions.

Measures

Role Variable Operationalization
IV Phone condition Random assignment; compliance 97 %
Mediator 1 Distraction 4‑item scale (e.g., “I felt distracted…”)
Mediator 2‑4 Time in store / Shelf attention / Loop diversion Same coding as Study 1
DV Purchases Basket value (SEK) + item count
Controls Satisfaction (trip & service) 1‑5 scales

Manipulation Check & Compliance

  • Non‑use group: 96.9 % obeyed (2 urgent calls).
  • Use group: 96.2 % used phones (~0.74 min; 4.8 % of trip).
  • Randomization + high compliance ⇒ strong internal validity.

Analysis Model

  • Serial mediation: Phone condition → Distraction → (Time | Fixations | Diversion) → Purchases.
  • PROCESS Models 4 & 6; 100 000 bootstrap iterations.

Key Results

Path β / Diff p / CI Supported
Phone → Distraction +0.85 <.001 ✔︎
Phone → Time +4.45 min .03 ✔︎ (H1a)
Phone → Fixations +25.84 .001 ✔︎ (H2a)
Phone → Diversion +0.28 .07† marginal
Mediations (H4a–c) Indirect CIs exclude 0 see text ✔︎ / †
Purchases diff +129 SEK .10 marginal but positive

Note: Age significantly moderates all three mediator paths; older shoppers show stronger effects.

Summary

  • Experimental replication confirms causal impact of phone use on distraction‑driven behaviors and spending.
  • Distraction is the first‑link mechanism; behavioral changes transmit the effect.
  • Age, department, and phone‑activity type act as boundary conditions.

Results Summary by Hypothesis

Hypothesis Study 1 Study 2 Verdict
H1a Time ↑ ✔︎ +4.6 min (p<.001) ✔︎ +4.45 min (p=.03) Supported
H1b Time → Spend ✔︎ β = 21.76 (p<.001) ✔︎ β = 31.45 (p<.001) Supported
H2a Fixations ↑ ✔︎ +17 % (p=.02) ✔︎ +25.84 (p=.001) Supported
H2b Fixations → Spend ✔︎ β = 3.97 (p<.001) ✔︎ β = 7.13 (p<.001) Supported
H3a Diversion ↑ ✔︎ +0.97 (p<.001) † +0.28 (p=.07) Partial
H3b Diversion → Spend ✔︎ β = 69.41 (p<.001) ✔︎ β = 238.39 (p<.001) Supported
H4 Mediations All three paths sig. Two sig., diversion marginal Largely Supported

(† = marginal, p < .10)

Theoretical Implications

  • Extends limited‑attention theory: Shows distraction can increase rather than impair goal performance when the task is open‑ended (shopping).
  • Links micro‑level gaze to macro sales: Connects eye‑tracking metrics to receipt data, bridging consumer psychology and retail analytics.
  • Aging & distraction: Demonstrates greater susceptibility among shoppers > 32 yrs, enriching lifespan cognition research.
  • Generalizable distraction mechanism: Parallel effects found for shopping‑with‑companions, hinting at a broader attentional conflict framework.

Integrated Discussion

  • Distraction loop: Phone use → cognitive split → linger, look, wander → more items bought.
  • Net‑positive experience: Satisfaction unchanged (Study 1) or only marginally lower (Study 2); benefits outweigh any friction.
  • Spatial nuance: Fresh‑food departments gain most; checkout losses confirm mobile blinders boundary.

Managerial Implications

  • Provide free Wi‑Fi & charging stations.
  • Embed QR codes / digital shelf labels near high‑margin SKUs.
  • Target older shoppers with phone‑friendly services (voice search).
  • Reframe showrooming: in‑store phone use can lift spend.

Limitations & Future Research

  • Grocery context; test department stores & electronics.
  • Manual coding limits eye‑tracking granularity.
  • Explore content of phone use (e.g., social vs price‑check).
  • Investigate motionless contexts (checkout, deli counter).

Class Discussion Prompts

  1. Should retailers ever limit phone use? Why / why not?
  2. How might AI partner apps evolve these dynamics?
  3. Where could “bad friction” still exist in this study’s design?

Thank You

Questions & Comments?

Download PDF